Documentation of 'ec.gp.breed.MutateDemotePipeline' Java class
MutateDemotePipeline
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ec.gp.breed

Class MutateDemotePipeline

  • All Implemented Interfaces:
    Prototype, Setup, SteadyStateBSourceForm, RandomChoiceChooserD, java.io.Serializable, java.lang.Cloneable

    public class MutateDemotePipeline
extends GPBreedingPipeline
    MutateDemotePipeline works very similarly to the DemoteNode algorithm described in Kumar Chellapilla, "A Preliminary Investigation into Evolving Modular Programs without Subtree Crossover", GP98, and is also similar to the "insertion" operator found in Una-May O'Reilly's thesis, "An Analysis of Genetic Programming".

    MutateDemotePipeline tries picks a random tree, then picks randomly from all the demotable nodes in the tree, and demotes one. If its chosen tree has no demotable nodes, or demoting its chosen demotable node would make the tree too deep, it repeats the choose-tree-then-choose-node process. If after tries times it has failed to find a valid tree and demotable node, it gives up and simply copies the individual.

    "Demotion" means to take a node n and insert a new node m between n and n's parent. n becomes a child of m; the place where it becomes a child is determined at random from all the type-compatible slots of m. The other child slots of m are filled with randomly-generated terminals. Chellapilla's version of the algorithm always places n in child slot 0 of m. Because this would be unneccessarily restrictive on strong typing, MutateDemotePipeline instead picks the slot at random from all available valid choices.

    A "Demotable" node means a node which is capable of demotion given the existing function set. In general to demote a node foo, there must exist in the function set a nonterminal whose return type is type-compatible with the child slot foo holds in its parent; this nonterminal must also have a child slot which is type-compatible with foo's return type.

    This method is very expensive in searching nodes for "demotability". However, if the number of types is 1 (the GP run is typeless) then the type-constraint-checking code is bypassed and the method runs a little faster.

    Typical Number of Individuals Produced Per produce(...) call
    ...as many as the source produces

    Number of Sources
    1

    Parameters

    base.tries
    int >= 1    		 (number of times to try finding valid pairs of nodes)
    base.maxdepth
    int >= 1    		 (maximum valid depth of a mutated tree)
    base.tree.0
    0 < int < (num trees in individuals), if exists    		 (tree chosen for mutation; if parameter doesn't exist, tree is picked at random)

    Default Base
    gp.breed.mutate-demote

    See Also:
    Serialized Form

    • Constructor Detail

      • MutateDemotePipeline

        public MutateDemotePipeline()

    • Method Detail

      • defaultBase

        public Parameter defaultBase()
        Description copied from interface: Prototype
        Returns the default base for this prototype. This should generally be implemented by building off of the static base() method on the DefaultsForm object for the prototype's package. This should be callable during setup(...).

      • numSources

        public int numSources()
        Description copied from class: BreedingPipeline
        Returns the number of sources to this pipeline. Called during BreedingPipeline's setup. Be sure to return a value > 0, or DYNAMIC_SOURCES which indicates that setup should check the parameter file for the parameter "num-sources" to make its determination.
        Specified by:
        numSources in class BreedingPipeline

      • setup

        public void setup(EvolutionState state,
                  Parameter base)
        Description copied from class: BreedingSource
        Sets up the BreedingPipeline. You can use state.output.error here because the top-level caller promises to call exitIfErrors() after calling setup. Note that probability might get modified again by an external source if it doesn't normalize right.

        The most common modification is to normalize it with some other set of probabilities, then set all of them up in increasing summation; this allows the use of the fast static BreedingSource-picking utility method, BreedingSource.pickRandom(...). In order to use this method, for example, if four breeding source probabilities are {0.3, 0.2, 0.1, 0.4}, then they should get normalized and summed by the outside owners as: {0.3, 0.5, 0.6, 1.0}.

        Specified by:
        setup in interface Prototype
        Specified by:
        setup in interface Setup
        Overrides:
        setup in class BreedingPipeline
        See Also:
        Prototype.setup(EvolutionState,Parameter)

      • produce

        public int produce(int min,
                   int max,
                   int start,
                   int subpopulation,
                   Individual[] inds,
                   EvolutionState state,
                   int thread)
        Description copied from class: BreedingSource
        Produces n individuals from the given subpopulation and puts them into inds[start...start+n-1], where n = Min(Max(q,min),max), where q is the "typical" number of individuals the BreedingSource produces in one shot, and returns n. max must be >= min, and min must be >= 1. For example, crossover might typically produce two individuals, tournament selection might typically produce a single individual, etc.
        Specified by:
        produce in class BreedingSource
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